The invention relates to a bidirectional signal processing method for the parallel transmission of digital transmit data streams in regular and singular radio channels of a multiple input-multiple output (MIMO) radio transmission system having nT transmit antennas and nR receive antennas.
A new field of research in the mobile radio technology sector has developed in the last several years proceeding from the discovery by Foschini and Gans that the capacity of mobile radio systems can be substantially increased by simultaneous use of a plurality of antennas at the transmitter and at the receiver. The structural principle of MIMO systems of the type was elucidated by earlier system approaches such as, for example, the known BLAST system G. D. Golden, G. J. Foschini, R. A. Valenzuela, P. W. Wolniansky “Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture”, Electronics Letters, vol. 35, no. 1, 1999 (“the Golden et al. reference”). Basically, a plurality of data streams are transmitted simultaneously on the same frequency, at the same time and where necessary also using the same pseudo-random noise code. This can be described by a vectorial transmission equation of the typey=Hx·n  (a)where x is a vector (identified by boldface notation) containing the simultaneously transmitted signals, y is a vector containing all the receive signals and n is a vector containing the isotropic receiver noise. As perfect a knowledge as possible of the transmission characteristics of the MIMO channel is essential in order to make full use of the available channel capacity, which is to say that the characteristics of the baseband channel matrix H with the complex transmission coefficients between each transmit and each receive antenna must be adequately known on the receive side and where necessary also on the transmit side. This can be achieved for example by known estimation methods on the basis of training sequences. In the following H is normalized such that the mean path loss is equal to 1. The essentially non-causal channel knowledge on the transmit side can be ascertained by the receive side for example by way of a return channel as long as the MIMO channel has not changed in time. In the time division duplex scheme (TDD, e.g. wireless LAN, UMTS-TDD) it is also possible, owing to the reciprocity of the channel, to use the channel knowledge from the opposite direction, as a result of which the return channel is no longer necessary. Building on this, linear or non-linear signal processing can then be applied on the receive side and where necessary also on the transmit side in order to separate the simultaneously transmitted data signals from one another again. This corresponds to a resolution of the linear equation system according to equation (a) based on the transmit vector x.
The currently known system approaches mostly proceed from the basic assumptions that on the transmit side the transmission behavior of the channel is not known and that the channel coefficients are independently and identically distributed (i.i.d.) random numbers according to a complex-value Gaussian distribution (Rayleigh channel). Channel knowledge at the receiver only is a useful assumption for frequency division duplex systems (FDD, many mobile radio networks, e.g. GSM, UMTS-FDD) in which the channel reciprocity does not apply and the above-mentioned return transmission of the channel coefficients would require too large a bandwidth. However, the algorithms for purely receive side separation of the data streams are all the more complex, the closer they approach the theoretically possible capacity limit. What is referred to as “maximum likelihood detection” (MLD) represents the optimum. However, the detection is so complicated and involved that it cannot be used at realistic data rates in realtime-capable systems. For this reason, less complex, suboptimal methods employing direct or recursive interference reduction are generally used, such as e.g. zero-forcing or V-BLAST methods the Golden et al. reference.
However, these suboptimal methods reveal a fundamental problem if the second assumption is infringed. The assumption of an i.i.d. Gaussian distribution is only applicable when a plurality of echo signals occur in the transmission channel. In environments with little dispersion, for example in the case of direct line-of-sight contact of a mobile receiving device in an open-sky location with a high, remote transmission mast, the entries in the channel matrix are no longer independently distributed and correlations occur between the receive signals. In the cited example the signals would only be phase-shifted relative to one another, but because of the almost identical distance from the transmitter would have approximately the same amplitude if no shadowings are present. It is well known that correlated channels generally have a lower transmission capacity than Rayleigh channels, so only relatively little information can be transmitted over them as well.
In the extreme case of occurring correlations the channel matrix H becomes singular, that is to say that no finite pseudo-inverse matrix exists any longer either. Consequently, signal processing methods based thereon cannot be used. Singularities can also occur even without correlations (“keyhole channels”). If the singular value decomposition (SVD(H)=U·D·VH) with a transition of the complex MIMO channel into a transmitter- and a receive-side transformation matrix V and U respectively is applied to such channels together with a quasi-diagonal matrix D in which the ordered singular values √λi derived from the subchannel-characteristic eigenvalues λi and otherwise zeroes are present on the left upper main diagonal, it can be seen that one or more of the singular values in the above example are close to zero. It will be explained briefly below, with reference to the very simple zero-forcing method, why signal processing in singular channels is so difficult. If SVD is used for example to form the left-side pseudo-inverse matrix H−1=V·D−1·UH, D−1 is also a quasi-diagonal matrix in which the inverse singular values 1/√λi and otherwise zeroes are present on the top left main diagonal. In the case of the receive-side signal reconstruction which corresponds to a resolution of the above equation system (a), in the zero-forcing method the received signal vector y is multiplied by H−1. The following holds (reconstructed signals are identified by an apostrophe):x′=x+H−1·n  (b)
From the viewpoint of the signal detector, therefore, the noise is multiplied by H−1. Thus, if one or more singular values as in the above example are equal to zero or are only close to zero, then the corresponding inverse values in H−1 are very large. Consequently the noise is hugely increased and many errors will be made in all data streams in the decision concerning a data symbol. In any case the noise is no longer isotropically distributed. With the exception of MLD, which is far too complex for practical applications however, all the known signal processing methods therefore have considerable problems in singular channels. A more general, mathematical description of this problem is based on capacity considerations. From these can be derived the effective dimension of the signal space (Effective Degrees Of Freedom, EDOF) which is determined, inter alia, by the ratio of the transmit power to the noise power at the receiver C. Chuah, G. J. Foschini, R. A. Valenzuela, D. Chizhi, J. Ling and J. M. Kahn, “Capacity Growth of Multi-Element Arrays in Indoor and Outdoor Wireless Channels”, Proc. Of IEEE Wireless Commun. and Networking Conf., Chicago, Ill., Sep. 23-28, 2000. This variable is also quite critically dependent on the size of the occurring singular values. The more singular values there are close to zero, the fewer are the dimensions that the signal space has, at least with a small signal-to-noise ratio. If a plurality of data streams are to be transmitted in parallel, then the number of data streams should therefore be matched to the effective dimension of the signal space EDOF. Otherwise serious errors will occur during the data transmission, at least with the simple transmission methods which rely on projection techniques.
Because of their relevance to mobile radio, to a very great extent only signal processing methods for FDD systems are considered at the present time. In these transmission systems there generally exists no channel knowledge on the transmit side, with signal processing being performed solely on the receive side. It is proposed in the literature, for example, to execute a “hard” switchoff of transmitter antennas when singular subchannels occur, in other words, simply not to transmit the corresponding data streams R. S. Blum, J. H. Winters “On optimum MIMO with Antenna Selection”, IEEE Communications Letters, vol. 6, no. 8, 2002, pp. 322-324. Power regulation does not take place here. In the Lucent proposal for the extension of the UMTS standard Seong Taek Chung, Angel Lozano, and Howard C. Huang “Approaching Eigenmode BLAST Channel Capacity Using V-BLAST with Rate and Power Feedback”, Proc. IEEE VTC Fall 2001, Atlantic City, N.J., Oct. 7-11, 2001, on the other hand, a “soft” switchoff of the transmit antennas is used. On the receive side the modulation and coding are matched to the transmission characteristics for each data stream separately. However, the selection of the modulation and coding method is made at the receiver on the basis of channel knowledge available there and with the inclusion of the characteristics of the spatial signal processing. This information is then communicated to the transmitter via a return channel, which requires less bandwidth compared with the transmission of the entire channel matrix. To date, however, there is an absence of reliable findings concerning how efficiently these methods are really able to handle the available capacity in singular channels.
The nearest related art on which the method is based is described in V. Jungnickel et al. “A MIMO WLAN based on Linear Channel Inversion”, IEE Coll. on MIMO Systems, Dec. 12, 2001. In this work it is proposed for a transmission system of the type described at the beginning that the LCI (Linear Channel Inversion) method be used for WLAN applications, particularly for the downlink. Since WLANs are mostly used within rooms, the preconditions for a MIMO system are similar to those in an i.i.d. Rayleigh transmission channel and consequently very good. The method per ser has the advantage that no channel knowledge or signal processing is necessary on the receive side, which means that inexpensive receiving devices can be used. In singular channels in particular, however, the LCI method—like the known ZF and BLAST methods—also reveals significant problems. Exclusively transmit-side signal processing, too, has the disadvantage that the characteristics of the signal processing, which can be predicted only with difficulty, are also included as input into the selection that is to be made of the modulation and coding method for the individual data streams. In this case all the data signals are distributed more or less evenly over essentially all eigenvectors. However, as a result of working with a fixed number of data streams, i.e. also at a constant data rate, the too small eigenvalues then increase the bit error rate for the entire transmission. The bit error rate therefore increases dramatically in singular subchannels. The cause for this is the inversion of singular values equal to or close to zero. In such channels a disproportionately high transmit power is then required in order to be able to transmit the transmit data reliably. The known LCI method, like the methods using exclusively receiver-side channel knowledge, can therefore be used satisfactorily only for channels with sufficient signal dispersion, which are usually to be found only in indoor areas or in heavily built-up areas (e.g. urban streets), and at a constant transmission data rate.